Ribhav G
- Research Program Mentor
MD candidate at University of Minnesota - Twin Cities
Expertise
Medicine, Infectious diseases, Public Health, Economic modeling, Data Science, Epidemiology, Meta-analysis, Health Economics,
Bio
Hey all! My name is Ribs (Ribhav) and I am a fourth year med student with a background in biomechanical engineering, health economics and infectious disease epidemiology from Stanford University. My research spans a multitude of domains with one commonality — leveraging AI to translate novel discoveries into clinical practices and health policies for under-served communities globally. To this effect, two domains of high interest are: 1) infectious disease transmission modeling to advising equitable, cost-effective prevention policies and 2) use of NLP to improve maternal and gender healthcare practices. Currently much of my work focuses on supporting immigrant and migrant populations. I am pursuing a career in academic medicine (take care of patients and continued research) — best of both worlds! Outside the hospital you can often find me running, climbing, reading mystery novels, or *attempting* to cook. Reach out if you have questions about medicine or my specific research, I’m always happy to talk!Project ideas
Vaccination coverage analysis & opportunities
Using open-access datasets, we can evaluate trends and distributions of vaccination coverage against common infections (e.g. DTaP) to identify geospatial and demographic gaps in immunity. This can provide public health departments nationally with a better sense of how to distribute new vaccination efforts.
Infectious disease transmission modeling
Pick an infectious disease and we can begin to build various transmission models to simulate how it would spread through a population and better understand disease dynamics. By layering theoretical intervention strategies on this, you can begin to explore the efficacy of possible health policies.
Gender bias in the news
Using natural language processing you can explore how men and women (or more broadly gender as a whole) is discussed in various forms of literature to identify particular biases. A clear example would be to explore gender-biases in coverage by regional newspapers both domestically and internationally